What are Recurrent Neural Networks in Computer Vision?

A Comprehensive guide into understanding the Recurrent Neural Networks (RNN) in Computer Vision

What is Computer Vision

Computer vision is a fascinating field of study that provides visual capabilities for artificial intelligence (AI). Computer vision (abbreviated as CV) is a subdomain of Artificial intelligence that helps the computer in interpreting digital image content by translating it into numerical information stored as multi-dimensional data. This data is readable by computers which in turn provide useful features of the image and therefore assist the decision-making.

In brief, the main objective served by combining Recurrent Neural Networks with the Computer Vision domain of artificial intelligence is to provide the machines with adequate tools to gather information from a set of pixels. In general, the operation involves the collection of image datasets, which serve as an input for Recurrent Neural Networks. For further details regarding the definition, use cases, applications, and requirements for deploying Recurrent Neural Networks (RNN) check the sections below.

What is RNN anyway?

In simplest terms, the Recurrent Neural Network is a machine or a system that provides sequential outputs that are updated based on the input elements and the previous state of the computation. Therefore, RNN is a type of artificial neural network (ANN) comprising nodes that are similar to the neural cells of a human brain, which have neural connections and regularly update the decisions/ outputs based on the new information received. An ANN comprises hierarchically arranged layers enabling deep-learning models to abstract features at different nodal levels.

Let us suppose an RNN model has three layers, where each layer comprises three nodes. Further, each node has parameters and functions according to the use case’s need. Figure 1 illustrates this example pictorially. Here, the pointed arrows depict the flow of information through the various layers, in which the neuron outputs are connected to the inputs of other neurons. Since the intermediate layer is connected to many, the depicted topology is called one-to-many. Sequentially, this RNN model can detect the patterns in input training data and learns from Neural Network-based machine translations.

What are some of its interesting applications?

Not just limited to visual interpretations but RNN enables the machine to perform speech recognition, stigmatization, and text prediction. The computer vision domain is especially helpful in the following: –

1. Figuring out the medical results, healthcare reports (such as MRI scan images), and Protein synthesis and homology detection.
2. Security & Surveillance (using object motion detection)
3. Personal electronics (smartphones, camera sensors)
4. Automobiles (self-driving cars or Advanced driver assistance systems) 
5. Weather forecasting.

For years, neural networks were limited to robotics and industrial control applications. However, computational power advancements have enabled billions of matrix operations per second. Essentially, the faster training time of neural networks is now possible, and the applications in several other aspects are unlocked (including transportation, health diagnostics, and automation in smart homes). 

One specifically interesting use case of the CV domain is image captioning. Generating automatic alt-captions for images on web pages, traffic surveillance reports, providing recommended texts for social media posts, generating tags for images, and object identification are few applications of image captions. Check out the article – What is Image Captioning in Computer Vision? to get more details regarding the generation of image captions using computer vision.

Why Recurrent processing for Intelligent vision over other Neural Networks?

• Recurrent processing has a very powerful feature of cleaning up noise (or occlusion in an image). This feature especially helps in dealing with Additive Gaussian noise and extracts only useful information (for example, the edges of an object). Therefore, recurrent processing provides us with tools to build an optimal linear filter that can learn detecting in noisy conditions.
• RNN refers to the type of network having an infinite impulse response and dynamic behavior. The infinite impulse recurrent network has a loop mechanism and cyclic graphs.
• Furthermore, the nodes have special characteristics provided as additional stored states by Long Short-Term Memory (LSTM) cells. Therefore, the Recurrent processing sequences has ability to remembers each and every data feature throughout processing time. Particularly, this is beneficial in prediction-based Artificial Intelligence (AI) systems. Because the machine remembers the previous context from previous inputs as well, it can help in Personal Digital Assistants (PDAs) in continued translation of context during visual data processing.
• Recurrent neural network possess the flexibility to be utilized with convolutional layers to effectively augment the pixel neighborhood information.

Best suited language for computer vision?

There are several choices of programming language to get started for first-hand implementation of computer vision. The top choices include- Python, C++, or MATLAB. Besides, the reader can take up a course on intermediate python learning. Python language is extensively deployed in building web apps utilizing computer vision programming in the artificial intelligence domain.

However, most beginner and experienced programmers use Python because Python provides flexibility with an extensive set of libraries.

Some of these important libraries are: –

• PyTesseract: Optical Character Recognition library that helps in embedded-text recognizing from an image. JPEG, JPG, PNG, GIF, etc are supported file formats.
• Scikit-Image: Contains a collection of open-source image processing algorithms.
• Keras: A high-level library of neural networks that hosts support for Theano and TensorFlow (TF).
• OpenCV: Most popular open-source library for computer vision, that brings along cutting edge video analysis tools alongside the image processing framework.

Conclusion

RNNs form an advanced use of Computer Vision as observed in several real-life applications. Most programmers learn only either of the two domains, whereas learning both can unlock many possibilities. We learned that – while RNN comprises nodes that mimic the neural cells of a human brain. On the other hand, Computer vision mimics the visual data collected via the human eye. The role of recurrent processing in computer vision is also helpful in performing image recognition in noisy conditions and a plurality of challenging conditions.

In addition, the neural networks provide painless tools for extracting object image features. This solves the burden of manual image feature extraction. At last, the reader is advised to explore other related neural network recognition methods such as YOLO and
RetinaNet. Happy Learning.